Evaluating spiking neural models in the classification of motor imagery EEG signals using short calibration sessions

Show simple item record

dc.contributor.author Salazar-Varas, R.
dc.contributor.author Vazquez, Roberto A.
dc.contributor.author ,
dc.contributor.other Universidad La Salle México
dc.creator R. Salazar-Varas;330284
dc.creator Roberto A. Vazquez;--
dc.creator ;
dc.date.accessioned 2018-08-03T16:44:26Z
dc.date.available 2018-08-03T16:44:26Z
dc.date.issued --6
dc.identifier.isbn J.R. Wolpaw, N. Birbaumer, D.J. McFarland, G. Pfurtscheller, T.M. Vaughan,Brain–computer interfaces for communication and control, Clin.Neurophysiol. 113 (2002) 767–791.[2] N.M. Norani, W. Mansor, L. Khuan, A review of signal processing in braincomputer interface system, in: IEEE EMBS Conference on BiomedicalEngineering and Sciences (IECBES), IEEE, 2010, pp. 443–449.[3] A. Bashashati, M. Fatourechi, R.K. Ward, G.E. Birch, A survey of signalprocessing algorithms in brain–computer interfaces based on electrical brainsignals, J. Neural Eng. 4 (2007) R32.[4] F. Lotte, M. Congedo, A. Lécuyer, F. Lamarche, B. Arnaldi, A review ofclassification algorithms for EEG-based brain-computer interfaces, J. NeuralEng. 4 (2007) R1.[5] S.N. Abdulkader, A. Atia, M.-S.M. Mostafa, Brain computer interfacing:applications and challenges, Egypt. Inform. J. 16 (2015) 213–230.[6] F. Popescu, B. Blankertz, K. Muller, Computational challenges for noninvasivebrain computer interfaces, IEEE Intell. Syst. 23 (2008) 78–79.[7] M. Arvaneh, C. Guan, K.K. Ang, C. Quek, Optimizing the channel selection andclassification accuracy in EEG-based BCI, IEEE Trans. Biomed. Eng. 58 (2011)1865–1873.[8] W. Ting, Y. Guo-zheng, Y. Bang-hua, S. Hong, EEG feature extraction based onwavelet packet decomposition for brain computer interface, Measurement 41(2008) 618–625.[9] Q. Wei, Y. Wang, X. Gao, S. Gao, Amplitude and phase coupling measures forfeature extraction in an EEG-based brain–computer interface, J. Neural Eng. 4(2007) 120.[10] S. Patidar, R.B. Pachori, A. Upadhyay, U.R. Acharya, An integrated alcoholicindex using tunable-q wavelet transform based features extracted from {EEG}signals for diagnosis of alcoholism, Appl. Soft Comput. 50 (2017) 71–78.[11] R. Dhiman, J. Saini, Priyanka, Biogeography based hybrid scheme forautomatic detection of epileptic seizures from {EEG} signatures, Appl. SoftComput. 51 (2017) 116–129.[12] K. Majumdar, Human scalp {EEG} processing: Various soft computingapproaches, Appl. Soft Comput. 11 (2011) 4433–4447.[13] J. Yang, H. Singh, E.L. Hines, F. Schlaghecken, D.D. Iliescu, M.S. Leeson, N.G.Stocks, Channel selection and classification of electroencephalogram signals:an artificial neural network and genetic algorithm-based approach, Artif.Intell. Med. 55 (2012) 117–126.[14] K. Aslan, H. Bozdemir, C. S¸ ahin, S.N. O˘gulata, R. Erol, A radial basis functionneural network model for classification of epilepsy using EEG signals, J. Med.Syst. 32 (2008) 403–408.[15] W. Maass, T.U. Graz, Networks of spiking neurons: the third generation ofneural network models, Neural Netw. 10 (1997) 1659–1671.[16] M.E. Hasselmo, C. Bodelón, B.P. Wyble, A proposed function for hippocampaltheta rhythm: separate phases of encoding and retrieval enhance reversal ofprior learning, Neural Comput. 14 (2002) 793–817.[17] S.J. Thorpe, R. Guyonneau, N. Guilbaud, J.-M. Allegraud, R. VanRullen,Spikenet: real-time visual processing with one spike per neuron,Neurocomputing 58–60 (2004) 857–864, Computational Neuroscience:Trends in Research 2004.[18] R. Vazquez, B. Girau, J. Quinton, Visual attention using spiking neural maps,in: The 2011 International Joint Conference on Neural Networks (IJCNN), pp.2164–2171.[19] R.A. Vazquez, B.A. Garro, Training spiking neural models using artificial beecolony, Comput. Intell. Neurosci. 2015 (2015), Article ID 947098.[20] A. Cachón, R.A. Vazquez, Tuning the parameters of an integrate and fireneuron via a genetic algorithm for solving pattern recognition problems,Neurocomputing 148 (2015) 187–197.[21] R. Vazquez, Izhikevich neuron model and its application in patternrecognition, Aust. J. Intell. Inf. Process. Syst. 11 (2010) 35–40.[22] A. Belatreche, L. P. Maguire, T. M. McGinnity, Pattern recognition with spikingneural networks and dynamic synapse, in: International FLINS Conference onApplied computational intelligence, pp. 205–210.[23] J. Wade, L. Mcdaid, J. Santos, H. Sayers, Swat: a spiking neural networktraining algorithm for classification problems, IEEE Trans. Neural Networks 21(2010) 1817–1830.[24] N. Kasabov, Evolving spiking neural networks for spatio-andspectro-temporal pattern recognition, in: 2012 6th IEEE InternationalConference Intelligent Systems (IS), pp. 27–32.[25] R.A. Vázquez, Pattern recognition using spiking neurons and firing rates, in:Á.F.K. Morales, G.R. Simari (Eds.), Advances in Artificial Intelligence –IBERAMIA 2010, 12th Ibero-American Conference on AI, Bahía Blanca,Argentina, November 1–5, 2010, Proceedings, volume 6433 of Lecture Notesin Computer Science, Springer, 2010, pp. 423–432.[26] S. Ghosh-Dastidar, H. Adeli, Improved spiking neural networks for EEGclassification and epilepsy and seizure detection, Integr. Comput.-Aided Eng.14 (2007) 187–212.[27] E. Capecci, N. Kasabov, G.Y. Wang, Analysis of connectivity in neucube spikingneural network models trained on {EEG} data for the understanding offunctional changes in the brain: a case study on opiate dependencetreatment, Neural Netw. 68 (2015) 62–77.[28] M.G. Doborjeh, G.Y. Wang, N.K. Kasabov, R. Kydd, B. Russell, A spiking neuralnetwork methodology and system for learning and comparative analysis ofEEG data from healthy versus addiction treated versus addiction not treatedsubjects, IEEE Trans. Biomed. Eng. 63 (2016) 1830–1841.[29] P. Goel, H. Liu, D. Brown, A. Datta, On the use of spiking neural network forEEG classification, Int. J. Knowl.-Based Intell. Eng. Syst. 12 (2008) 295–304.[30] R. Salazar-Varas, D. Gutiérrez, An optimized feature selection andclassification method for using electroencephalographic coherence inbrain-computer interfaces, Biomed. Signal Process. Control 18 (2015) 11–18.[31] S. Sanei, J.A. Chambers, EEG Signal Processing, Wiley, 2008.[32] L. Faes, G.D. Pinna, A. Porta, R. Maestri, G. Nollo, Surrogate data analysis forassessing the significance of the coherence function, IEEE Trans. Biomed. Eng.51 (2004) 1156–1166.[33] W. Gerstner, W.M. Kistler, Spiking Neuron Models: Single Neurons,Populations, Plasticity, Cambridge University Press, 2002.[34] A.L. Hodgkin, The local electric changes associated with repetitive action in anon-medullated axon, J. Physiol. 107 (1948) 165–181.[35] L. Abbott, Lapicque’s introduction of the integrate-and-fire model neuron(1907), Brain Res. Bull. 50 (1999) 303–304.[36] R. FitzHugh, Impulses and physiological states in theoretical models of nervemembrane, Biophys. J. 1 (1961) 445–466.[37] R.M. Rose, J.L. Hindmarsh, The assembly of ionic currents in a thalamic neuronI. The three-dimensional model, Proc. R. Soc. Lond. B: Biol. Sci. 237 (1989)267–288.[38] E.M. Izhikevich, Dynamical Systems in Neuroscience: The Geometry ofExcitability and Bursting, Computational Neuroscience, MIT Press, 2007.[39] R.A. Vázquez, B.A. Garro, Training spiking neurons by means of particleswarm optimization, in: Proceedings of the Second International Conferenceon Advances in Swarm Intelligence – Volume Part I, ICSI’11, Springer-Verlag,Berlin, Heidelberg, 2011, pp. 242–249.[40] R. Vazquez, Training spiking neural models using cuckoo search algorithm, in:2011 IEEE Congress on Evolutionary Computation (CEC), pp. 679–686.[41] R.A. Vazquez, G. Sandoval, J. Ambrosio, How to generate the input current forexciting a spiking neural model using the cuckoo search algorithm, in: X.-S.Yang (Ed.), Cuckoo Search and Firefly Algorithm: Theory and Applications,Springer International Publishing, Cham, 2014, pp. 155–178.[42] B.A. Garro, H. Sossa, R.A. Vázquez, Design of artificial neural networks usingdifferential evolution algorithm, in: K.W. Wong, B.S.U. Mendis, A.Bouzerdoum (Eds.), Neural Information Processing. Models and Applications– 17th International Conference, ICONIP 2010, Sydney, Australia, November22–25, 2010, Proceedings, Part II, volume 6444 of Lecture Notes in ComputerScience, Springer, 2010, pp. 201–208.[43] B.A. Garro, H. Sossa, R.A. Vázquez, Artificial neural network synthesis bymeans of artificial bee colony (ABC) algorithm, in: Proceedings of the IEEECongress on Evolutionary Computation, CEC 2011, New Orleans, LA, USA, 5–8June, 2011, IEEE, 2011, pp. 331–338.[44] B.A. Garro, R.A. Vazquez, Designing artificial neural networks using particleswarm optimization algorithms, Comput. Intell. Neurosci. 2015 (2015), ArticleID 369298.[45] B.A. Garro, H. Sossa, R.A. Vázquez, Evolving neural networks: A comparisonbetween differential evolution and particle swarm optimization, in: Y. Tan, Y.Shi, Y. Chai, G. Wang (Eds.), Advances in Swarm Intelligence - SecondInternational Conference, ICSI 2011, Chongqing, China, June 12–15, 2011,Proceedings, Part I, volume 6728 of Lecture Notes in Computer Science,Springer, 2011, pp. 447–454.[46] B.A. Garro, H. Sossa, R.A. Vázquez, Back-propagation vs. particle swarmoptimization algorithm: which algorithm is better to adjust the synapticweights of a feed-forward ANN? Int. J. Artif. Intell. 7 (2011) 208–218.[47] R.C. Eberhart, Y. Shi, J. Kennedy, Swarm Intelligence (The Morgan KaufmannSeries in Evolutionary Computation), 1st ed., Morgan Kaufmann, 2001.[48] G. Dornhege, B. Blankertz, G. Curio, K.-R. Muller, Boosting bit rates innoninvasive EEG single-trial classifications by feature combination andmulticlass paradigms, IEEE Trans. Biomed. Eng. 51 (2004) 993–1002.[49] J.d.R. Millan, On the need for on-line learning in brain–computer interfaces,in: Proceedings. 2004 IEEE International Joint Conference
dc.identifier.uri http://repositorio.udlap.mx/xmlui/handle/123456789/13338
dc.description.sponsorship The authors would like to thank Universidad La Salle México forthe economic support under grant number NEC-03/15 and IMC-08/16.
dc.description.statementofresponsibility Estudiantes
dc.description.statementofresponsibility Investigadores
dc.language eng
dc.publisher Elsevier
dc.relation Versión aceptada
dc.relation.haspart http://www.bbci.de/competition/iii/desc_IVa.html
dc.rights En Embargo
dc.rights.uri http://creativecommons.org/licenses/by-nd/4.0
dc.subject Brain-computer interface
dc.subject pattern recognition
dc.subject coherence
dc.subject.classification INGENIERÍA Y TECNOLOGÍA
dc.title Evaluating spiking neural models in the classification of motor imagery EEG signals using short calibration sessions
dc.type Artículo

Files in this item

This item appears in the following Collection(s)

Show simple item record

En Embargo Except where otherwise noted, this item's license is described as En Embargo


Advanced Search


My Account